Deep supervised anomaly detection for generalized face forgery detection
Fan Zhang, Ting Yang, Lin Cao, Kangning Du, Yanan Guo, Peiran Song, Chen Shao · Pattern Recognition · 2025
Nowadays, face forgery poses a significant threat to societal security, making the development of effective countermeasures imperative. Though most existing methods adopt neural networks to automatically extract discriminative features for forgery detection and have achieved promising results, significant challenges remain. Namely, when detecting forgery faces generated by unseen forgery methods, the detection performance degrades significantly, indicating poor generalization capability. To address such limitation, a novel deep supervised anomaly detection for generalized face forgery detection (DAGFD) is proposed in this paper. Specifically, the artifact map detector optimized by triplet focal loss and metric-softmax loss is first used to locate the forgery regions and obtain artifact maps. Next, forgery detection is reformulated from the supervised anomaly detection perspective, and the artifact map score is calculated to detect forgery videos. Furthermore, mean square error (MSE) loss is used to minimize the artifact map score of real samples while increase the one of forgery samples to generalize well to unseen forgery methods. Also, circle loss is used for auxiliary classifier to learn more discriminative artifact features. Finally, the experimental results demonstrate that the proposed method’s detection accuracy is better than other state-of-the-art methods.